Interactive Web‐based Data Visualization and Analysis Tool for Synthetizing on‐farm Research Networks Data

Laurent, Anabelle; Lyu, Xiaodan; Kyveryga, Peter; Makowski, David; Hofmann, Heike; Miguez, Fernando E.

doi:10.1002/jrsm.1440

Cited by 8 publications

(4 citation statements)

References 17 publications

(29 reference statements)

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“…For example, the Making Better Fertiliser Decisions for Cropping (BFDC) Systems in Australia project has made data of thousands of fertilization experiments available through a web‐tool called the BFDC Interrogator (Dowling & Spiers, 2013), where users can explore soil test calibrations across multiple combinations of crops and environmental conditions (Bell et al., 2013; Brennan & Bell, 2013; Watmuff et al., 2013). Similarly, one of the most recent and relevant examples in the United States is the Interactive Summaries of On‐Farm Strip Trials (ISOFAST) (Laurent et al., 2020) and Economic Value of On‐Farm Studies (EVOS) (Iowa Soybean Association, 2022) tools, friendly open‐access web‐interfaces that allow users to browse summaries of on‐farm experiments and simulate economic impact for a wide variety of crop management practices in corn ( Zea mays L.) and soybean in Iowa.…”

Section: Discussionmentioning

confidence: 99%

Interactive soybean variable‐rate seeding simulator for farmers

et al. 2022

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Seed expense is a critical component affecting soybean (Glycine max L.) production cost. Achieving potential benefits of adjusting seeding rates by yield environment requires translation from theory to practice through decision support tools. This article introduces the soybean variable-rate seeding (VRS) simulator, an interactive web-tool designed to facilitate VRS decisions. A group of agronomists and farmers provided input and feedback to the tool's original design, usability, interactive features, and tool outputs. The simulator generates variable rate seeding recommendations by (a) aggregating and classifying 3 yr of historical soybean yield maps using either yield level (low, medium, or high) or temporal yield stability (stable or unstable) classes, and (b) estimating the difference between the farmers current uniform seeding rate and the optimal seeding rates calibrated for different yield environments of the U.S. Midwest region. For each yield environment, the tool suggests a seeding rate change accompanied by an expected yield increase. Marginal economic and break-even price analyses of each VRS prescription choice are also provided considering soybean plant survival rate, seed cost, market price, and crop production costs. A step-by-step description is provided using a dataset of real, historical, on-farm soybean yield maps to illustrate the tool's features and limitations. Simulated results indicate that compared to typical fixed seeding rate choices, following the tool's recommendations could improve profits between US$5 and 57 ha -1 . The development of this tool contributes to a growing trend of user-friendly interfaces that assist in translating core agronomic knowledge into practical solutions.

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Section: Discussionmentioning

confidence: 99%

Interactive soybean variable‐rate seeding simulator for farmers

et al. 2022

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“…The OHSPEIMS provides a simple, fast, free solution for constructing a private, highly customized database management system. Shiny is often used to build interactive Web apps, and more and more apps are being published online to simplify some complex analysis processes and data visualization, such as genomic analysis 23 , proteomic analysis 24 , transcriptome analysis 25 , prediction model 26 , complex model 27 and visualization system for COVID-19 pandemic 27 . Hence, it is entirely possible to build a customized dataset management system using Shiny.…”

Section: Discussionmentioning

confidence: 99%

An Occupational Health Survey for Port Employees in Shenzhen and A Dataset Management System based on R

Wang

Huang³

et al. 2022

Preprint

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Background: Port employees is a huge occupational group in industrial economy but the occupational health problem does not receive enough attention and the relative data is still deficient. Hence, the aim of the study was to survey the occupational health condition for port employees and to construct a relative dataset. Method: A cross-sectional study was implemented among the population in a Shenzhen port, they were required to undergo occupational physical examination and questionnaires to learn about physical condition and other information. Description analysis were used to describe the data, and missing value analysis and Cronbach's alpha coefficient were utilized to evaluated the data quality. And management system based on Shiny was constructed to manage and analyze the dataset. Result: A total of 5245 participants involved in this study, 3211 of them received occupational physical examinations, 3946 participants received the questionnaire, and 1912 received the both. Quality analysis suggested that the total missing rate of these three datasets were 10.76%, 0% and 4.78%, respectively. And the total Cronbach's alpha confidence of Effort-Reward Imbalance Questionnaire and National Health Literacy Monitoring Questionnaire was 0.808. Furthermore, a dataset management system with preview overview, selection, output and summary functions was constructed. Conclusion：Occupational Health Survey for Port Employees is a reliable survey and it's system can be used to manage and analyze the dataset, however, further optimization and improvement are still required.

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“…Kandel et al (2016) suggested that although fungicides can increase soybean yield in the apparent absence of disease, only 14% of the trials were economically profitable. A web-application, called ISOFAST, synthetizing data from replicated on-farm trials, provides break-even economic analysis of foliar fungicides on maize and soybean (Laurent et al, 2019(Laurent et al, , 2020a. Of the 19 foliar fungicide products evaluated, only six had a >50% probability of exceeding the break-even cost (considering grain market price and application cost averaged over the last 10 years).…”

Section: Mean Yield Log Ratio Of Soybeanmentioning

confidence: 99%

“…For soybean, data were also unbalanced as 82% of the trials were OFTs. A future investigation could consider accounting for covariables, such as fungicide application timing and maize hybrid, to examine their effects on yield response (Kyveryga et al, 2013;Laurent et al, 2020a). Wise et al, (2019) have shown that yield response to a fungicide is affected by the growth stage of the crop at the time of application.…”

Section: Mean Yield Log Ratio Of Soybeanmentioning

confidence: 99%

A yield comparison between small-plot and on-farm foliar fungicide trials in soybean and maize

Laurent

Heaton

Kyveryga

et al. 2022

Agron. Sustain. Dev.

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Agronomic research provides management recommendations based on small-plot trials (SPTs) and on-farm trials (OFTs) with very different characteristics. SPTs are traditionally conducted at agricultural experiment stations by research institutes or universities, while OFTs are conducted under commercial-scale conditions and managed by farmers using their own equipment. Several researchers claimed that discrepancies could occur between these two types of trials, which can make the extrapolation of results from SPTs to the farm level difficult. In our study, we conducted an extensive comparison of small-plot and on-farm trials to analyze the effect of foliar fungicide application on maize and soybean yields. We collected data on maize and soybean from five US states. Analysis of the soybean data showed similar mean yield responses and within-trial standard deviation to fungicide application between 479 OFTs and 83 SPTs. For maize, our comparison of 300 OFTs and 114 SPTs showed similar mean yield response in both. Nevertheless, the within-trial standard deviation was three times smaller in on-farm compared to small-plot trials. On the other hand, the between-trial standard deviation (measuring the variability of the effects of fungicide application across different environments) was almost twice as large in SPTs than in OFTs for both crops. Hence, the differences in the effects of fungicide on yield were similar whether they were estimated using OFTs or SPTs for both crops. This implies that OFTs can potentially detect significant yield differences with fewer replicates and thus reduce the cost of data generation. We argue that SPTs can be seen as a preliminary step before scaling up to OFTs to facilitate technology transfer and extrapolate the results in real farming conditions.

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Interactive Web‐based Data Visualization and Analysis Tool for Synthetizing on‐farm Research Networks Data

Cited by 8 publications

References 17 publications

Interactive soybean variable‐rate seeding simulator for farmers

Interactive soybean variable‐rate seeding simulator for farmers

An Occupational Health Survey for Port Employees in Shenzhen and A Dataset Management System based on R

A yield comparison between small-plot and on-farm foliar fungicide trials in soybean and maize

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